Summary of Audio-infused Automatic Image Colorization by Exploiting Audio Scene Semantics, By Pengcheng Zhao et al.
Audio-Infused Automatic Image Colorization by Exploiting Audio Scene Semantics
by Pengcheng Zhao, Yanxiang Chen, Yang Zhao, Zhao Zhang
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method for automatic image colorization using audio as an additional semantic cue. The method, called Audio-Infused Automatic Image Colorization (AIAIC), consists of three stages: pre-training a colorization network with color image semantics, learning color semantic correlations between audio and visual scenes, and feeding the audio semantic representation into the pretrained network for final colorization. This approach can effectively improve performance on difficult-to-colorize scenes, demonstrating the potential of multi-modal learning in computer vision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to make black-and-white pictures colorful using sound. It works by first training a computer program with lots of colored images and their corresponding sounds. Then, it uses this training to learn how colors and sounds are related. Finally, it applies this knowledge to colorize new grayscale images that don’t have any sound information. The result is more accurate and realistic colors for pictures that were hard to color otherwise. |
Keywords
» Artificial intelligence » Multi modal » Semantics
